Handwriting recognition involves converting written strokes into a sequence of symbols. Recently, there has been interest in performing such recognition on handwritten mathematical expressions.
Recognition systems for handwritten mathematical expressions have utilized a multi-stage system. Initially, the input strokes representing the mathematical expression are grouped into segments based on a likelihood that they may represent a symbol. In general, a set of n-best groupings are identified from the mathematical expression. Each of these groupings is then applied to a symbol recognition sub-system that identifies likely symbols that could be represented by the strokes in each grouping. This results in a set of n-best sequences of symbols. Each of the n-best sequences of symbols are then applied to a structure analysis that analyzes the relative positioning of the symbols to each other to identify, for example, symbols that are in the numerator or denominator of a fraction, symbols that are in a matrix, and symbols that are either a subscript or a superscript of other symbols. A semantic structure analysis is then performed to identify the semantic meaning of certain symbols, including which symbols represent operands and which symbols represent variables. The result of the semantic structure analysis is a recognized mathematical expression.
The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. In general, mathematical expressions are more difficult to recognize because the information contained in a mathematical expression is dependent not only on the symbols within the expression, but on their positioning relative to each other.